Journal of Water and Wastewater Science and Engineering

Journal of Water and Wastewater Science and Engineering

Examining the Contribution of Wastewater Treatment Plant Features on the Results of Artificial Intelligence-Based Models Using SHAP

Document Type : Original Article

Authors
1 Professor, Water Engineering Department, Faculty of Civil Engineering, Tabriz University, Tabriz, Iran.
2 M.Sc. Student, Water Engineering Department, Faculty of Civil Engineering, Tabriz University, Tabriz, Iran.
3 Associate Professor, Water Engineering Department, Faculty of Civil Engineering, Tabriz University, Tabriz, Iran.
Abstract
Models can greatly contribute to the optimization and control of treatment processes. The present study investigates the Biological Oxygen Demand (BODeff) and Chemical Oxygen Demand (CODeff) of the effluent from the Tabriz municipal wastewater treatment plant (WWTP). This plant utilizes an activated sludge process with diffused aeration. Using Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models, daily data from the WWTP are analyzed to assess these key pollution indicators. Prior to modeling, input features are denoised using a Simple Moving Average (SMA) technique. Both linear and nonlinear relationships between features are examined to select optimal model inputs. The conversion factors obtained using ANN and LSTM were 1.66 and 1.65 for BOD to COD, and 1.32 and 1.33 for Total Dissolved Solids (TDS) to Electrical Conductivity (EC), respectively. These values are appropriate and demonstrate the models' accuracy in estimating relationships. Furthermore, to interpret feature importance, the novel and emerging method of Explainable Artificial Intelligence (XAI) are employed. BODeff and CODeff with a one-day lag, as well as TDS and EC, were identified as high-impact features.
Keywords

 
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Volume 10, Issue 2
Summer 2025
Pages 55-70

  • Receive Date 19 January 2025
  • Revise Date 06 April 2025
  • Accept Date 23 April 2025